Introducción a R - Sesión 2

# Introducción a R - Sesión 2
## Un curso (bastante) rápido
### Octavio Medina
### 2021-11-25

---

# Visualización con ggplot2
---
class: inverse middle center

Antes de empezar:

```r
install.packages("tidyverse")
install.packages("hrbrthemes")
install.packages("viridis")
```

Y luego:

```r
library("tidyverse")
library("hrbrthemes")
library("viridis")
```

---

# Por qué visualizar?

Por qué molestarse? **Dadme razones.**

- Procesamiento cognitivo

- Reconocimiento de patrones

- Detección de problemas

- Comunicación de conclusiones

---

# Por qué visualizar?

]

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.panel[.panel-name[Gorila Virtual]

]
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.panel[.panel-name[Tabla]

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  <thead class="gt_col_headings">
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      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">estado</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">esperanza_vida</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">anyo</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">Louisiana</td>
<td class="gt_row gt_right">71.6</td>
<td class="gt_row gt_right">1980</td></tr>
    <tr><td class="gt_row gt_left">Massachusetts</td>
<td class="gt_row gt_right">74.8</td>
<td class="gt_row gt_right">1980</td></tr>
    <tr><td class="gt_row gt_left">DC</td>
<td class="gt_row gt_right">68.9</td>
<td class="gt_row gt_right">1980</td></tr>
    <tr><td class="gt_row gt_left">Louisiana</td>
<td class="gt_row gt_right">74.1</td>
<td class="gt_row gt_right">2000</td></tr>
    <tr><td class="gt_row gt_left">Massachusetts</td>
<td class="gt_row gt_right">78.2</td>
<td class="gt_row gt_right">2000</td></tr>
    <tr><td class="gt_row gt_left">DC</td>
<td class="gt_row gt_right">72.1</td>
<td class="gt_row gt_right">2000</td></tr>
    <tr><td class="gt_row gt_left">Louisiana</td>
<td class="gt_row gt_right">76.1</td>
<td class="gt_row gt_right">2018</td></tr>
    <tr><td class="gt_row gt_left">Massachusetts</td>
<td class="gt_row gt_right">80.5</td>
<td class="gt_row gt_right">2018</td></tr>
    <tr><td class="gt_row gt_left">DC</td>
<td class="gt_row gt_right">78.6</td>
<td class="gt_row gt_right">2018</td></tr>
  </tbody>
  
  
</table>
</div>

]
.panel[.panel-name[Gráfica]

]
]
]

---

## La gramática de los gráficos

- La librería de gráficos más popular (y más mejor!) de R es `ggplot2`. Sigue principios basados en la **gramática de los gráficos**.

- La gramática de los gráficos es un principio que nos permite definir y organizar un plot según sus diferentes capas.

- Esto permite **combinar piezas** para crear prácticamente todo tipo de gráficas.

]

.pull-right[
<div class="figure">
<img src="data:image/png;base64,#gog.png" alt="Una gráfica es como una cebolla." width="100%" />
<p class="caption">Una gráfica es como una cebolla.</p>
</div>
]
---

## Las capas de una gráfica: lo imprescindible

---

## Las capas de una gráfica: lo imprescindible

- **Data**: vuestra base de datos o dataframe, siempre que sea posible en formato `tidy`.

- **Aesthetic mapping**: Los `aesthetic mappings` describen cómo las variables de los datos se asignan (mapean) a distintas propiedades visuales (la estética).

- **Geometries**: la geometría que queremos usar para mostrar nuestros datos.

---

## Ejemplo

- **Data:** `gapminder`
- **Aesthetic mapping:** `mean_life_exp` al eje x, `continent` al eje y 
- **Geometries:** columna, o `geom_col()`

<img src="data:image/png;base64,#sesion-2_files/figure-html/unnamed-chunk-9-1.png" width="100%" />
---

## Ejemplo

- **Data:** ?
- **Aesthetic mapping:** ?
- **Geometries:** ?

---

## Ejemplo

- **Data:** ?
- **Aesthetic mapping:** ?
- **Geometries:** ?

---

## Los mappings

- `x`
- `y`
- `color`
- `fill`
- `alpha`
- `size`

]

## Los geoms

- `geom_point()`
- `geom_histogram()`
- `geom_line()`
- `geom_bar()`
- `geom_smooth()`

]

---

## Scatterplot con `geom_point()`

- Un gráfico humilde pero resultón

```r
gap_82 <- gapminder %>% 
  filter(year == 1982)

ggplot(data = gap_82,
*      mapping =  aes(x = gdpPercap,
*                       y = lifeExp)) +
* geom_point()
```
]

.pull-right[
<img src="data:image/png;base64,#sesion-2_files/figure-html/unnamed-chunk-13-1.png" width="100%" />
]

---

## Scatterplot con `geom_point()`

- Se pueden acumular geoms! Recordad, todo es una cebolla.

```r
gap_82 <- gapminder %>% 
  filter(year == 1982)

ggplot(data = gap_82,
       mapping =  aes(x = gdpPercap,  
                        y = lifeExp)) + 
* geom_point() +
* geom_smooth()
```
]

.pull-right[
<img src="data:image/png;base64,#sesion-2_files/figure-html/unnamed-chunk-15-1.png" width="100%" />
]

---

## Barras con `geom_bar()` y `geom_col()`

- Las barras son un poco especiales, porque se pueden usar para **contar observaciones** o para **mostrar otras variables.**

- Para lo primero, usaremos `geom_bar()` (ojo con los mappings!), para lo demás `geom_col()` suele ser más cómodo.

```r
ggplot(data = gap_82,
       mapping =  aes(x = gdpPercap,  
                        y = lifeExp)) + 
* geom_bar()
```
]

.pull-right[
<img src="data:image/png;base64,#sesion-2_files/figure-html/unnamed-chunk-17-1.png" width="100%" />
]

---

## Histograma con `geom_histogram()`

- Notáis algo similar?

```r
ggplot(data = gap_82,
*      mapping =  aes(x = gdpPercap)) +
* geom_histogram()
```
]

.pull-right[
<img src="data:image/png;base64,#sesion-2_files/figure-html/unnamed-chunk-19-1.png" width="100%" />
]

---
class: center middle

# Probando los mappings: breve demostración

---
class: inverse center middle

# themes y facets: dos elementos más

---

# `themes` and co.

```r
install.packages("hrbrthemes")
library(hrbrthemes)

ggplot(data = gap_82,
       mapping =  aes(x = gdpPercap)) +  
  geom_histogram() +
* theme_ipsum()
```

]

.pull-right[
<img src="data:image/png;base64,#sesion-2_files/figure-html/unnamed-chunk-21-1.png" width="100%" />
]

---

# `themes` and co.

```r
ggplot(data = gap_82,
       mapping =  aes(x = gdpPercap)) +  
  geom_histogram() +
* theme_minimal() +
* facet_wrap(~continent)
```

]

.pull-right[
<img src="data:image/png;base64,#sesion-2_files/figure-html/unnamed-chunk-23-1.png" width="100%" />
]
---

# Escalas de colores

- Para añadir una escala discreta para `color`, podemos añadir `scale_color_*`. Si es para fill, entonces `scale_fill_*`.

- Las escalas `viridis`, del paquete del mismo nombre, suelen ser una buena opción. Pero hay muchas!

```r
ggplot(data = gap_82,
       mapping =  aes(x = gdpPercap,
                      y = lifeExp,
                      color = continent)) +  
  geom_point() +
  theme_ipsum() + 
* facet_wrap(~continent) +
* scale_color_viridis_d()
```

]

.pull-right[
<img src="data:image/png;base64,#sesion-2_files/figure-html/unnamed-chunk-25-1.png" width="80%" />
]

---

## Anatomía de un ggplot

---

```r
*gapminder
```
]
 
.panel2-my_gap-auto[

```
# A tibble: 1,704 × 6
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.
 2 Afghanistan Asia       1957    30.3  9240934      821.
 3 Afghanistan Asia       1962    32.0 10267083      853.
 4 Afghanistan Asia       1967    34.0 11537966      836.
 5 Afghanistan Asia       1972    36.1 13079460      740.
 6 Afghanistan Asia       1977    38.4 14880372      786.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
# … with 1,694 more rows
```
]

---
count: false

```r
gapminder %>%
* filter(year == 1982)
```
]
 
.panel2-my_gap-auto[

```
# A tibble: 142 × 6
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1982    39.9 12881816      978.
 2 Albania     Europe     1982    70.4  2780097     3631.
 3 Algeria     Africa     1982    61.4 20033753     5745.
 4 Angola      Africa     1982    39.9  7016384     2757.
 5 Argentina   Americas   1982    69.9 29341374     8998.
 6 Australia   Oceania    1982    74.7 15184200    19477.
 7 Austria     Europe     1982    73.2  7574613    21597.
 8 Bahrain     Asia       1982    69.1   377967    19211.
 9 Bangladesh  Asia       1982    50.0 93074406      677.
10 Belgium     Europe     1982    73.9  9856303    20980.
# … with 132 more rows
```
]

---
count: false

```r
gapminder %>%
  filter(year == 1982) %>%
* ggplot()
```
]
 
.panel2-my_gap-auto[
<img src="data:image/png;base64,#sesion-2_files/figure-html/my_gap_auto_03_output-1.png" width="100%" />
]

---
count: false

```r
gapminder %>%
  filter(year == 1982) %>%
  ggplot() +
* aes(x = gdpPercap)
```
]
 
.panel2-my_gap-auto[
<img src="data:image/png;base64,#sesion-2_files/figure-html/my_gap_auto_04_output-1.png" width="100%" />
]

---
count: false

```r
gapminder %>%
  filter(year == 1982) %>%
  ggplot() +
  aes(x = gdpPercap) +
* aes(y = lifeExp)
```
]
 
.panel2-my_gap-auto[
<img src="data:image/png;base64,#sesion-2_files/figure-html/my_gap_auto_05_output-1.png" width="100%" />
]

---
count: false

```r
gapminder %>%
  filter(year == 1982) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
* geom_point()
```
]
 
.panel2-my_gap-auto[
<img src="data:image/png;base64,#sesion-2_files/figure-html/my_gap_auto_06_output-1.png" width="100%" />
]

---
count: false

```r
gapminder %>%
  filter(year == 1982) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
  geom_point() +
* aes(color = continent)
```
]
 
.panel2-my_gap-auto[
<img src="data:image/png;base64,#sesion-2_files/figure-html/my_gap_auto_07_output-1.png" width="100%" />
]

---
count: false

```r
gapminder %>%
  filter(year == 1982) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
  geom_point() +
  aes(color = continent) +
* aes(size = pop)
```
]
 
.panel2-my_gap-auto[
<img src="data:image/png;base64,#sesion-2_files/figure-html/my_gap_auto_08_output-1.png" width="100%" />
]

---
count: false

```r
gapminder %>%
  filter(year == 1982) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
  geom_point() +
  aes(color = continent) +
  aes(size = pop) +
* theme_ipsum()
```
]
 
.panel2-my_gap-auto[
<img src="data:image/png;base64,#sesion-2_files/figure-html/my_gap_auto_09_output-1.png" width="100%" />
]

---
count: false

```r
gapminder %>%
  filter(year == 1982) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
  geom_point() +
  aes(color = continent) +
  aes(size = pop) +
  theme_ipsum() +
* labs(title = "PIB y esperanza de vida en 1982",
*      subtitle = "Datos del proyecto Gapminder, creado por Hans Rosling",
*      x = "PIB per cápita",
*      y = "Esperanza de vida",
*      color = "Continente",
*      size = "Población")
```
]
 
.panel2-my_gap-auto[
<img src="data:image/png;base64,#sesion-2_files/figure-html/my_gap_auto_10_output-1.png" width="100%" />
]

---
count: false

```r
gapminder %>%
  filter(year == 1982) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
  geom_point() +
  aes(color = continent) +
  aes(size = pop) +
  theme_ipsum() +
  labs(title = "PIB y esperanza de vida en 1982",
       subtitle = "Datos del proyecto Gapminder, creado por Hans Rosling",
       x = "PIB per cápita",
       y = "Esperanza de vida",
       color = "Continente",
       size = "Población") +
* scale_color_viridis_d()
```
]
 
.panel2-my_gap-auto[
<img src="data:image/png;base64,#sesion-2_files/figure-html/my_gap_auto_11_output-1.png" width="100%" />
]

---
count: false

```r
gapminder %>%
  filter(year == 1982) %>%
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
  geom_point() +
  aes(color = continent) +
  aes(size = pop) +
  theme_ipsum() +
  labs(title = "PIB y esperanza de vida en 1982",
       subtitle = "Datos del proyecto Gapminder, creado por Hans Rosling",
       x = "PIB per cápita",
       y = "Esperanza de vida",
       color = "Continente",
       size = "Población") +
  scale_color_viridis_d()
```
]
 
.panel2-my_gap-auto[
<img src="data:image/png;base64,#sesion-2_files/figure-html/my_gap_auto_12_output-1.png" width="100%" />
]

---

# Preguntas?